class_docstr: Class for masking of Niimg-like objects using seeds.
  
  NiftiSpheresMasker is useful when data from given seeds should be
  extracted. Use case: Summarize brain signals from seeds that were
  obtained from prior knowledge.
  
  Parameters
  ----------
  seeds : :obj:`list` of triplet of coordinates in native space
      Seed definitions. List of coordinates of the seeds in the same space
      as the images (typically MNI or TAL).
  
  radius : :obj:`float`, default=None
      Indicates, in millimeters, the radius for the sphere around the seed.
      By default signal is extracted on a single voxel.
  
  mask_img : Niimg-like object, default=None
      See :ref:`extracting_data`.
      Mask to apply to regions before extracting signals.
  
  allow_overlap : :obj:`bool`, default=False
      If False, an error is raised if the maps overlaps (ie at least two
      maps have a non-zero value for the same voxel).
  %(smoothing_fwhm)s
  %(standardize_maskers)s
  %(standardize_confounds)s
  high_variance_confounds : :obj:`bool`, default=False
      If True, high variance confounds are computed on provided image with
      :func:`nilearn.image.high_variance_confounds` and default parameters
      and regressed out.
  %(detrend)s
  %(low_pass)s
  %(high_pass)s
  %(t_r)s
  dtype : {dtype, "auto"}, optional
      Data type toward which the data should be converted. If "auto", the
      data will be converted to int32 if dtype is discrete and float32 if it
      is continuous.
  %(memory)s
  %(memory_level1)s
  %(verbose0)s
  %(masker_kwargs)s
  
  Attributes
  ----------
  n_elements_ : :obj:`int`
      The number of seeds in the masker.
  
      .. versionadded:: 0.9.2
  
  seeds_ : :obj:`list` of :obj:`list`
      The coordinates of the seeds in the masker.
  
  reports : boolean, default=True
       If set to True, data is saved in order to produce a report.
  
  See Also
  --------
  nilearn.maskers.NiftiMasker
code: def _check_fitted(self):
      if not hasattr(self, "seeds_"):
          raise ValueError(
              f"It seems that {self.__class__.__name__} "
              "has not been fitted. "
              "You must call fit() before calling transform()."
          )
  
imports: import warnings
  import numpy
  from joblib import Memory
  from scipy import sparse
  from sklearn import neighbors
  from nilearn import image, masking
  from nilearn._utils import CacheMixin, fill_doc, logger
  from nilearn._utils.class_inspect import get_params
  from nilearn._utils.niimg import img_data_dtype
  from nilearn._utils.niimg_conversions import check_niimg_3d, check_niimg_4d, safe_get_data
  from nilearn.datasets import load_mni152_template
  from nilearn.maskers._utils import compute_middle_image
  from nilearn.maskers.base_masker import BaseMasker, _filter_and_extract
label_desc: Use image node_id 6 for calling the NiftiSpheresMasker obj's underlying member method code with example usage: obj._check_fitted() without return types